Several works have been proposed for semi-supervised AD.
Deep SAD [1]} extends the deep one-class classification method DSVDD [2]} into the semi-supervised setting.
However, their approach is non-interpretable and performs even worse than the unsupervised One-Class SVM for tabular data, while the DIAD outperforms it.
DevNet [3]} formulates the AD problem into a regression problem and achieves better sample complexity under limited labeled data.
[4]} trains an embedding similar to BERT [5]} combined with consistency loss [6]} and achieves the state-of-the-art semi-supervised performance in tabular data.
See Table REF  for a comparison.
